Document Type
Article
Author ORCID Identifier
Vladimir Kulyukin https://orcid.org/0000-0002-8778-5175
Daniel Coster https://orcid.org/0000-0002-4801-5637
Journal/Book Title/Conference
Sensors
Volume
23
Issue
5
Publisher
MDPI AG
Publication Date
2-26-2023
First Page
1
Last Page
29
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable.
Recommended Citation
Kulyukin, V.; Coster, D.; Tkachenko, A.; Hornberger, D.; Kulyukin, A. Ambient Electromagnetic Radiation as Predictor of Honey Bee (Apis mellifera) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency. Sensors 2023, 23, 2584. https://doi.org/10.3390/s23052584
